Publication:
Double sigmoid activation function for fault detection in wind turbine generator using artificial neural network
Double sigmoid activation function for fault detection in wind turbine generator using artificial neural network
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Date
2025-06
Authors
Noor Fazliana Fadzail
Samila Mat Zali
Ernie Che Mid
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Publisher
Iran University of Science and Technology
Research Projects
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Abstract
The activation function has gained popularity in the research community since it is the most crucial component of the artificial neural network (ANN) algorithm. However, the existing activation function is unable to accurately capture the value of several parameters that are affected by the fault, especially in wind turbines (WT). Therefore, a new activation function is suggested in this paper, which is called the double sigmoid activation function to capture the value of certain parameters that are affected by the fault. The fault detection in WT with a doubly fed induction generator (DFIG) is the basis for the ANN algorithm model that is presented in this study. The ANN model was developed in different activation functions, namely linear and double sigmoid activation functions to evaluate the effectiveness of the proposed activation function. The findings indicate that the model with a double sigmoid activation function has greater accuracy than the model with a linear activation function. Moreover, the double sigmoid activation function provides an accuracy of more than 82% in the ANN algorithm. In conclusion, the simulated response demonstrates that the proposed double sigmoid activation function in the ANN model can effectively be applied in fault detection for DFIG based WT model.
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Keywords
Activation Function,
Artificial Neural Network,
Doubly Fed Induction Generator,
Wind turbine,
Machine learning,
Fault detection